Many robots are programmed to utilize one or more end effectors to grasp objects. For example, a robot may utilize a grasping end effector such as an “impactive” grasping end effector or “ingressive” grasping end effector (e.g., physically penetrating an object using pins, needles, etc.) to pick up an object from a first location, move the object to a second location, and drop off the object at the second location. Some additional examples of robot end effectors that may grasp objects include “astrictive” grasping end effectors (e.g., using suction or vacuum to pick up an object) and one or more “contigutive” grasping end effectors (e.g., using surface tension, freezing, or adhesive to pick up an object), to name just a few.
While humans innately know how to correctly grasp many different objects, determining an appropriate manner to grasp an object for manipulation of that object may be a difficult task for robots. Despite the difficulty, various approaches have been proposed in which robots can grasp various objects. However, many of those approaches may suffer from one or more drawbacks, such as not leveraging one or more grasp parameters determined through physical manipulation(s) of robot(s) by user(s), not utilizing grasp parameters associated with an object model of an object to be grasped, not taking certain force grasp parameters into account in grasping an object, etc. Additional and/or alternative drawbacks of these and/or other approaches may be presented.